Censuses provide a generally simple, and often used, form of collecting population data of a particular region. Since ancient Roman times, controlling bodies utilized censuses for a variety of reasons, such as counting the population that lives in a particular region. While the data collected in a simple census provides useful information, the lack of logical correlation of the population data to other factors of the region other than the boundaries of the region, limits the potential extractable information from the population data. Censuses generally report the aggregate number of people residing in a region. These regions typically include entire towns, cities, states, and even countries, each of which contain vast amounts of land area where no population lives. Consequently, simple censuses of relatively large regions provide little, if any, information regarding where individual members of a population generally live within that large region.
In many instances, people who constitute the population of a region live in highly populated areas of that region, leaving other areas of that region relatively unpopulated. Nevertheless, simple censuses do not account for the reality of densely populated sub-regions and sparsely populated sub-regions that are within a larger region. While a simple census provide some usefulness in determining the aggregate number of people residing in a region, some simple censuses inherently limit a systematic correlation of the location of a population throughout that region.
To account for the inherent limitations of a simple census, some methods have been developed to systematically distribute members of a region with a known population to sub-regions within a region. Past methods attempted to distribute members of a population based upon geographic features. Such a geographic correlation suffers from inherent limitations due to the relatively large size of geographic features when compared to the living area of an individual member of a population. For example, some methods correlate a higher incidence of population within a given region for areas with close proximity to such geographical features as impervious surfaces represented by roads, houses, and other features. Other geographic features that have been used to correlate incidence of population within a certain region include slope, land cover type, and intensity of nighttime lights. Nevertheless, such individual spatial relationships cannot reliably predict a population distribution for various reasons.
First, the relationship of the proximity of a relatively densely populated subregion to geographic features varies from region to region. For example, in a typical metropolis, there may be a positive correlation between the proximity of a densely populated sub-region to a major road. For example, correlating a road to population density would distribute the population census for the shown region to be concentrated around the road. While this may be an accurate description in some regions, in other regions an opposite correlation may exist such as a desert region having vast road networks with little population. Additionally, in a farmland region, there may be a negative correlation between the proximity of a populated sub-region to a major road. Further, in a suburban region, no correlation may exist between the proximity of a relatively densely populated sub-region to a major road. Therefore, while geographic features can provide a useful correlation for distributing a population within a certain region, individual geographic features alone do not provide a predictable and reliable relation for distributing a population.
Additionally, while censuses usually limit data collection to locations where members live, even correlations with geographic regions do not account for the reality of transient populations. In the mobile world of today, transient populations exist on town, city, or even state level. Many people travel at least a few miles to work everyday, yet spend most of their nights in another location. Therefore, transient populations produce an affect on day time verse night time populations, an affect that is exacerbated with refinement of the region's resolution.
A system that accounts for spatial and temporally refined population distribution data can provide a more accurate presentation of the population distribution for day or night. Such accurate information provides beneficial uses in a variety of applications in counter-terrorism, homeland security, consequence analysis, epidemiology, exposure analysis, urban sprawl detection, estimation of populations affected by global sea level rise, and emergency planning and management for natural disasters, nuclear, biological, and chemical accidents. Terrorism, natural disasters, and technological accidents can strike anywhere on earth, yet can have impacts on limited areas, such as neighborhoods, city blocks, and even buildings. Population distribution estimates on such a fine resolution help in planning for and responding to such events.